EP4681197A1 - Deep ahs: a deep learning approach to acoustic howling suppression - Google Patents

Abstract

A method and apparatus comprising computer code configured to cause a processor or processors to receive an audio signal obtained from a microphone, input the audio signal into a neural-network based AHS model, wherein the neural-network based AHS model is trained using a training audio signal, and output an AHS signal from the neural-network based AHS model in which AHS is applied to the audio signal, wherein the AHS signal is a version of the audio signal in which acoustic howling noise of the audio signal is suppressed and target audio of the audio signal is sustained.

Description

DEEP AHS: A DEEP LEARNING APPROACH TO ACOUSTIC HOWLING SUPPRESSION

CROSS REFERENCE TO RELATED APPLICATION

[1] The present application claims priority to US Application No. 18/327,418 filed on June 1, 2023, which is hereby expressly incorporated by reference, in its entirety, into the present application.

BACKGROUND

1. Field

[2] The present disclosure is directed a set of advanced audio technologies acoustic howling suppression (AHS).

2. Description of Related Art

[3] Acoustic howling has become a crucial problem in video/audio conference and acoustic amplification systems.

[4] Howling may arise due to the coupling between a microphone and a loudspeaker such as when there exists positive feedback therebetween. Specifically, the microphone signal from a microphone in an audio system may be played out through a loudspeaker that is exposed in a same space and then picked up again by the same microphone, forming a closed acoustic loop.

[5] If not properly handled, this playback signal may be looped back repeatedly and result in a shrill sound at frequencies that have unity or larger loop gain. This phenomenon is known as howling. [6] Howling is a crucial problem for video/audio conferences and acoustic amplification systems such as hearing aids and karaoke. It is not only harmful to our auditory system but also destructive to the amplification equipment. Therefore, howling mitigation has become a crucial problem in video/audio conference, hearing aids, karaoke and other acoustic amplification systems.

[7] Many AHS solutions have been proposed to address this problem, including gain control, notch filter (NF), and adaptive feedback cancellation (AFC). The gain reduction method can be achieved by either manually reducing the volume of an amplifier or altering the position of audio devices. However, such methods are with restricted applications and unsuitable in scenarios that require high acoustic amplification. The NF methods attenuate howling by adjusting their filter coefficients to form a null at frequencies where howling appears. However, the NF methods require accurate detection of howling and inherently distort the target sound and even introduce unexpected howling frequencies. AFC attenuates howling by estimating the acoustic path between the loudspeaker and microphone using adaptive filters. Because the target signal and playback signal are highly correlated, de-correlation techniques may be usually required in AFC methods, which, however, inevitably distorts speech quality.

[8] And for any of those reasons there is therefore a desire for technical solutions to such problems that arose in computer audio technology.

SUMMARY

[9] There is included a method and apparatus comprising memory configured to store computer program code and a processor or processors configured to access the computer program code and operate as instructed by the computer program code. The computer program is configured to cause the processor implement receiving code configured to cause the at least one processor to receive an audio signal obtained from a microphone; inputting code configured to cause the at least one processor to input the audio signal into a neural -network based AHS model, wherein the neural-network based AHS model is trained using a training audio signal; and outputting code configured to cause the at least one processor to output an AHS signal from the neural -network based AHS model in which AHS is applied to the audio signal, wherein the AHS signal is a version of the audio signal in which acoustic howling noise of the audio signal is suppressed and target audio of the audio signal is sustained.

[10] According to exemplary embodiments, the neural -network based AHS model is trained with a loss function comprising a combination of a scale-invariance signal-to-distortion ratio (SISDR) in time domain and mean absolute error (MAE) of spectrum magnitude in frequency domain.

[11] According to exemplary embodiments, the neural -network based AHS model is trained with teacher-forced learning.

[12] According to exemplary embodiments, the neural -network based AHS model comprises a first gated recurrent unit (GRU) layer configured to apply an estimate to the audio signal.

[13] According to exemplary embodiments, the first GRU layer comprises 257 hidden units and two one-dimensional (ID) convolution layers.

[14] According to exemplary embodiments, the neural -network based AHS model further comprises a second GRU layer configured to receive an output of the first GRU and to generate a covariance matrix of the acoustic howling noise and the target audio.

[15] According to exemplary embodiments, the second GRU layer is configured to receive both the audio signal and the output from the first GRU. [16] According to exemplary embodiments, the neural -network based AHS model further comprises an enhancement fdter estimation layer comprising a self-attentive recurrent neural network (RNN) configured to provide a speech enhancement filter to an input channel of the audio signal.

[17] According to exemplary embodiments, inputs to the first GRU layer comprise the audio signal, a normalized log-power spectra (LPS) of the audio signal, a temporal correlation of the audio signal, a frequency correlation of the audio signal, and a channel covariance of the audio signal.

[18] According to exemplary embodiments, the inputs to the first GRU layer comprise a concatenation of the temporal correlation of the audio signal, the frequency correlation of the audio signal, and the channel covariance of the audio signal

BRIEF DESCRIPTION OF THE DRAWINGS

[19] Further features, nature, and various advantages of the disclosed subject matter will be more apparent from the following detailed description and the accompanying drawings in which:

[20] Fig. l is a schematic illustration of a diagram in accordance with embodiments;

[21] Fig. 2 is a simplified block diagram in accordance with embodiments;

[22] Fig. 3 is a simplified illustration in accordance with embodiments;

[23] Fig. 4 is a simplified illustration in accordance with embodiments;

[24] Fig. 5 is a simplified illustration in accordance with embodiments;

[25] Fig. 6 is a simplified illustration in accordance with embodiments;

[26] Fig. 7 is a simplified illustration in accordance with embodiments; [27] Fig. 8 is a simplified flow diagram in accordance with embodiments;

[28] Fig. 9 is a simplified flow diagram in accordance with embodiments;

[29] Fig. 10 is a simplified diagram in accordance with embodiments;

[30] Fig. 11 is a simplified flow diagram in accordance with embodiments;

[31] Fig. 12 is simplified diagrams in accordance with embodiments;

[32] Fig. 13 is a simplified illustration in accordance with embodiments.

DETAILED DESCRIPTION

[33] The proposed features discussed below may be used separately or combined in any order. Further, the embodiments may be implemented by processing circuitry (e.g., one or more processors or one or more integrated circuits). In one example, the one or more processors execute a program that is stored in a non-transitory computer-readable medium.

[34] Fig. 1 illustrates a simplified block diagram of a communication system 100 according to an embodiment of the present disclosure. The communication system 100 may include at least two terminals 102 and 103 interconnected via a network 105. For unidirectional transmission of data, a first terminal 103 may code video data at a local location for transmission to the other terminal 102 via the network 105. The second terminal 102 may receive the coded video data of the other terminal from the network 105, decode the coded data and display the recovered video data. Unidirectional data transmission may be common in media serving applications and the like.

[35] Fig. 1 illustrates a second pair of terminals 101 and 104 provided to support bidirectional transmission of coded video that may occur, for example, during videoconferencing. For bidirectional transmission of data, each terminal 101 and 104 may code video data captured at a local location for transmission to the other terminal via the network 105. Each terminal 101 and

104 also may receive the coded video data transmitted by the other terminal, may decode the coded data and may display the recovered video data at a local display device.

[36] In Fig. 1, the terminals 101, 102, 103 and 104 may be illustrated as servers, personal computers and smart phones but the principles of the present disclosure are not so limited.

Embodiments of the present disclosure find application with laptop computers, tablet computers, media players and/or dedicated video conferencing equipment. The network 105 represents any number of networks that convey coded video data among the terminals 101, 102, 103 and 104, including for example wireline and/or wireless communication networks. The communication network 105 may exchange data in circuit-switched and/or packet-switched channels.

Representative networks include telecommunications networks, local area networks, wide area networks and/or the Internet. For the purposes of the present discussion, the architecture and topology of the network 105 may be immaterial to the operation of the present disclosure unless explained herein below.

[37] Fig. 2 illustrates, as an example for an application for the disclosed subject matter, the placement of a video encoder and decoder in a streaming environment. The disclosed subject matter can be equally applicable to other video enabled applications, including, for example, video conferencing, digital TV, storing of compressed video on digital media including CD, DVD, memory stick and the like, and so on.

[38] A streaming system may include a capture subsystem 203, that can include a video source 201, for example a digital camera, creating, for example, an uncompressed video sample stream 213. That sample stream 213 may be emphasized as a high data volume when compared to encoded video bitstreams and can be processed by an encoder 202 coupled to the video source 201, which may be for example a camera as discussed above. The encoder 202 can include hardware, software, or a combination thereof to enable or implement aspects of the disclosed subject matter as described in more detail below. The encoded video bitstream 204, which may be emphasized as a lower data volume when compared to the sample stream, can be stored on a streaming server 205 for future use. One or more streaming clients 212 and 207 can access the streaming server 205 to retrieve copies 208 and 206 of the encoded video bitstream 204. A client 212 can include a video decoder 211 which decodes the incoming copy of the encoded video bitstream 208 and creates an outgoing video sample stream 210 that can be rendered on a display 209 or other rendering device (not depicted). In some streaming systems, the video bitstreams 204, 206 and 208 can be encoded according to certain video coding/compression standards. Examples of those standards are noted above and described further herein.

[39] Fig. 3 may be a functional block diagram of a video decoder 300 according to an embodiment of the present disclosure.

[40] A receiver 302 may receive one or more codec video sequences to be decoded by the decoder 300; in the same or another embodiment, one coded video sequence at a time, where the decoding of each coded video sequence is independent from other coded video sequences. The coded video sequence may be received from a channel 301, which may be a hardware/software link to a storage device which stores the encoded video data. The receiver 302 may receive the encoded video data with other data, for example, coded audio data and/or ancillary data streams, that may be forwarded to their respective using entities (not depicted). The receiver 302 may separate the coded video sequence from the other data. To combat network jitter, a buffer memory 303 may be coupled in between receiver 302 and entropy decoder / parser 304 (“parser” henceforth). When receiver 302 is receiving data from a store/forward device of sufficient bandwidth and controllability, or from an isosychronous network, the buffer 303 may not be needed, or can be small. For use on best effort packet networks such as the Internet, the buffer 303 may be required, can be comparatively large and can advantageously of adaptive size.

[41] The video decoder 300 may include a parser 304 to reconstruct symbols 313 from the entropy coded video sequence. Categories of those symbols include information used to manage operation of the decoder 300, and potentially information to control a rendering device such as a display 312 that is not an integral part of the decoder but can be coupled to it. The control information for the rendering device(s) may be in the form of Supplementary Enhancement Information (SEI messages) or Video Usability Information (VUI) parameter set fragments (not depicted). The parser 304 may parse / entropy-decode the coded video sequence received. The coding of the coded video sequence can be in accordance with a video coding technology or standard, and can follow principles well known to a person skilled in the art, including variable length coding, Huffman coding, arithmetic coding with or without context sensitivity, and so forth. The parser 304 may extract from the coded video sequence, a set of subgroup parameters for at least one of the subgroups of pixels in the video decoder, based upon at least one parameters corresponding to the group. Subgroups can include Groups of Pictures (GOPs), pictures, tiles, slices, macroblocks, Coding Units (CUs), blocks, Transform Units (TUs), Prediction Units (PUs) and so forth. The entropy decoder / parser may also extract from the coded video sequence information such as transform coefficients, quantizer parameter values, motion vectors, and so forth.

[42] The parser 304 may perform entropy decoding / parsing operation on the video sequence received from the buffer 303, so to create symbols 313. The parser 304 may receive encoded data, and selectively decode particular symbols 313. Further, the parser 304 may determine whether the particular symbols 313 are to be provided to a Motion Compensation Prediction unit 306, a scaler / inverse transform unit 305, an Intra Prediction Unit 307, or a loop fdter 311.

[43] Reconstruction of the symbols 313 can involve multiple different units depending on the type of the coded video picture or parts thereof (such as: inter and intra picture, inter and intra block), and other factors. Which units are involved, and how, can be controlled by the subgroup control information that was parsed from the coded video sequence by the parser 304. The flow of such subgroup control information between the parser 304 and the multiple units below is not depicted for clarity.

[44] Beyond the functional blocks already mentioned, decoder 300 can be conceptually subdivided into a number of functional units as described below. In a practical implementation operating under commercial constraints, many of these units interact closely with each other and can, at least partly, be integrated into each other. However, for the purpose of describing the disclosed subject matter, the conceptual subdivision into the functional units below is appropriate.

[45] A first unit is the scaler / inverse transform unit 305. The scaler / inverse transform unit 305 receives quantized transform coefficient as well as control information, including which transform to use, block size, quantization factor, quantization scaling matrices, etc. as symbol(s) 313 from the parser 304. It can output blocks comprising sample values, that can be input into aggregator 310.

[46] In some cases, the output samples of the scaler / inverse transform 305 can pertain to an intra coded block; that is: a block that is not using predictive information from previously reconstructed pictures, but can use predictive information from previously reconstructed parts of the current picture. Such predictive information can be provided by an intra picture prediction unit 307. In some cases, the intra picture prediction unit 307 generates a block of the same size and shape of the block under reconstruction, using surrounding already reconstructed information fetched from the current (partly reconstructed) picture 309. The aggregator 310, in some cases, adds, on a per sample basis, the prediction information the intra prediction unit 307 has generated to the output sample information as provided by the scaler / inverse transform unit 305.

[47] In other cases, the output samples of the scaler / inverse transform unit 305 can pertain to an inter coded, and potentially motion compensated block. In such a case, a Motion Compensation Prediction unit 306 can access reference picture memory 308 to fetch samples used for prediction. After motion compensating the fetched samples in accordance with the symbols 313 pertaining to the block, these samples can be added by the aggregator 310 to the output of the scaler / inverse transform unit (in this case called the residual samples or residual signal) so to generate output sample information. The addresses within the reference picture memory form where the motion compensation unit fetches prediction samples can be controlled by motion vectors, available to the motion compensation unit in the form of symbols 313 that can have, for example X, Y, and reference picture components. Motion compensation also can include interpolation of sample values as fetched from the reference picture memory when subsample exact motion vectors are in use, motion vector prediction mechanisms, and so forth.

[48] The output samples of the aggregator 310 can be subject to various loop filtering techniques in the loop filter unit 311. Video compression technologies can include in-loop filter technologies that are controlled by parameters included in the coded video bitstream and made available to the loop filter unit 311 as symbols 313 from the parser 304, but can also be responsive to meta-information obtained during the decoding of previous (in decoding order) parts of the coded picture or coded video sequence, as well as responsive to previously reconstructed and loop-fdtered sample values.

[49] The output of the loop fdter unit 311 can be a sample stream that can be output to the render device 312 as well as stored in the reference picture memory 557 for use in future interpicture prediction.

[50] Certain coded pictures, once fully reconstructed, can be used as reference pictures for future prediction. Once a coded picture is fully reconstructed and the coded picture has been identified as a reference picture (by, for example, parser 304), the current reference picture 309 can become part of the reference picture buffer 308, and a fresh current picture memory can be reallocated before commencing the reconstruction of the following coded picture.

[51] The video decoder 300 may perform decoding operations according to a predetermined video compression technology that may be documented in a standard, such as ITU-T Rec. H.265. The coded video sequence may conform to a syntax specified by the video compression technology or standard being used, in the sense that it adheres to the syntax of the video compression technology or standard, as specified in the video compression technology document or standard and specifically in the profiles document therein. Also necessary for compliance can be that the complexity of the coded video sequence is within bounds as defined by the level of the video compression technology or standard. In some cases, levels restrict the maximum picture size, maximum frame rate, maximum reconstruction sample rate (measured in, for example megasamples per second), maximum reference picture size, and so on. Limits set by levels can, in some cases, be further restricted through Hypothetical Reference Decoder (HRD) specifications and metadata for HRD buffer management signaled in the coded video sequence. [52] In an embodiment, the receiver 302 may receive additional (redundant) data with the encoded video. The additional data may be included as part of the coded video sequence(s). The additional data may be used by the video decoder 300 to properly decode the data and/or to more accurately reconstruct the original video data. Additional data can be in the form of, for example, temporal, spatial, or signal-to-noise ratio (SNR) enhancement layers, redundant slices, redundant pictures, forward error correction codes, and so on.

[53] Fig. 4 may be a functional block diagram of a video encoder 400 according to an embodiment of the present disclosure.

[54] The encoder 400 may receive video samples from a video source 401 (that is not part of the encoder) that may capture video image(s) to be coded by the encoder 400.

[55] The video source 401 may provide the source video sequence to be coded by the encoder (303) in the form of a digital video sample stream that can be of any suitable bit depth (for example: 8 bit, 10 bit, 12 bit, ...), any colorspace (for example, BT.601 Y CrCB, RGB, ...) and any suitable sampling structure (for example Y CrCb 4:2:0, Y CrCb 4:4:4). In a media serving system, the video source 401 may be a storage device storing previously prepared video. In a videoconferencing system, the video source 401 may be a camera that captures local image information as a video sequence. Video data may be provided as a plurality of individual pictures that impart motion when viewed in sequence. The pictures themselves may be organized as a spatial array of pixels, wherein each pixel can comprise one or more samples depending on the sampling structure, color space, etc. in use. A person skilled in the art can readily understand the relationship between pixels and samples. The description below focuses on samples. [56] According to an embodiment, the encoder 400 may code and compress the pictures of the source video sequence into a coded video sequence 410 in real time or under any other time constraints as required by the application. Enforcing appropriate coding speed is one function of Controller 402. Controller controls other functional units as described below and is functionally coupled to these units. The coupling is not depicted for clarity. Parameters set by controller can include rate control related parameters (picture skip, quantizer, lambda value of rate-distortion optimization techniques, .. .), picture size, group of pictures (GOP) layout, maximum motion vector search range, and so forth. A person skilled in the art can readily identify other functions of controller 402 as they may pertain to video encoder 400 optimized for a certain system design.

[57] Some video encoders operate in what a person skilled in the art readily recognizes as a “coding loop.” As an oversimplified description, a coding loop can consist of the encoding part of an encoder 400 (“source coder” henceforth) (responsible for creating symbols based on an input picture to be coded, and a reference picture(s)), and a (local) decoder 406 embedded in the encoder 400 that reconstructs the symbols to create the sample data that a (remote) decoder also would create (as any compression between symbols and coded video bitstream is lossless in the video compression technologies considered in the disclosed subject matter). That reconstructed sample stream is input to the reference picture memory 405. As the decoding of a symbol stream leads to bit-exact results independent of decoder location (local or remote), the reference picture buffer content is also bit exact between local encoder and remote encoder. In other words, the prediction part of an encoder “sees” as reference picture samples exactly the same sample values as a decoder would “see” when using prediction during decoding. This fundamental principle of reference picture synchronicity (and resulting drift, if synchronicity cannot be maintained, for example because of channel errors) is well known to a person skilled in the art. [58] The operation of the “local” decoder 406 can be the same as of a “remote” decoder 300, which has already been described in detail above in conjunction with Fig. 3. Briefly referring also to Fig. 4, however, as symbols are available and en/decoding of symbols to a coded video sequence by entropy coder 408 and parser 304 can be lossless, the entropy decoding parts of decoder 300, including channel 301, receiver 302, buffer 303, and parser 304 may not be fully implemented in local decoder 406.

[59] An observation that can be made at this point is that any decoder technology except the parsing/entropy decoding that is present in a decoder also necessarily needs to be present, in substantially identical functional form, in a corresponding encoder. The description of encoder technologies can be abbreviated as they are the inverse of the comprehensively described decoder technologies. Only in certain areas a more detail description is required and provided below.

[60] As part of its operation, the source coder 403 may perform motion compensated predictive coding, which codes an input frame predictively with reference to one or more previously-coded frames from the video sequence that were designated as “reference frames.” In this manner, the coding engine 407 codes differences between pixel blocks of an input frame and pixel blocks of reference frame(s) that may be selected as prediction reference(s) to the input frame.

[61] The local video decoder 406 may decode coded video data of frames that may be designated as reference frames, based on symbols created by the source coder 403. Operations of the coding engine 407 may advantageously be lossy processes. When the coded video data may be decoded at a video decoder (not shown in Fig. 4), the reconstructed video sequence typically may be a replica of the source video sequence with some errors. The local video decoder 406 replicates decoding processes that may be performed by the video decoder on reference frames and may cause reconstructed reference frames to be stored in the reference picture memory 405, which may be for example a cache. In this manner, the encoder 400 may store copies of reconstructed reference frames locally that have common content as the reconstructed reference frames that will be obtained by a far-end video decoder (absent transmission errors).

[62] The predictor 404 may perform prediction searches for the coding engine 407. That is, for a new frame to be coded, the predictor 404 may search the reference picture memory 405 for sample data (as candidate reference pixel blocks) or certain metadata such as reference picture motion vectors, block shapes, and so on, that may serve as an appropriate prediction reference for the new pictures. The predictor 404 may operate on a sample block-by-pixel block basis to find appropriate prediction references. In some cases, as determined by search results obtained by the predictor 404, an input picture may have prediction references drawn from multiple reference pictures stored in the reference picture memory 405.

[63] The controller 402 may manage coding operations of the source coder 403, which may be for example a video coder, including, for example, setting of parameters and subgroup parameters used for encoding the video data.

[64] Output of all aforementioned functional units may be subjected to entropy coding in the entropy coder 408. The entropy coder translates the symbols as generated by the various functional units into a coded video sequence, by loss-less compressing the symbols according to technologies known to a person skilled in the art as, for example Huffman coding, variable length coding, arithmetic coding, and so forth. [65] The transmitter 409 may buffer the coded video sequence(s) as created by the entropy coder 408 to prepare it for transmission via a communication channel 411, which may be a hardware/software link to a storage device which would store the encoded video data. The transmitter 409 may merge coded video data from the source coder 403 with other data to be transmitted, for example, coded audio data and/or ancillary data streams (sources not shown).

[66] The controller 402 may manage operation of the encoder 400. During coding, the controller 402 may assign to each coded picture a certain coded picture type, which may affect the coding techniques that may be applied to the respective picture. For example, pictures often may be assigned as one of the following frame types:

[67] An Intra Picture (I picture) may be one that may be coded and decoded without using any other frame in the sequence as a source of prediction. Some video codecs allow for different types of Intra pictures, including, for example Independent Decoder Refresh Pictures. A person skilled in the art is aware of those variants of 1 pictures and their respective applications and features.

[68] A Predictive picture (P picture) may be one that may be coded and decoded using intra prediction or inter prediction using at most one motion vector and reference index to predict the sample values of each block.

[69] A Bi-directionally Predictive Picture (B Picture) may be one that may be coded and decoded using intra prediction or inter prediction using at most two motion vectors and reference indices to predict the sample values of each block. Similarly, multiple-predictive pictures can use more than two reference pictures and associated metadata for the reconstruction of a single block. [70] Source pictures commonly may be subdivided spatially into a plurality of sample blocks (for example, blocks of 4 x 4, 8 x 8, 4 x 8, or 16 x 16 samples each) and coded on a block-by- block basis. Blocks may be coded predictively with reference to other (already coded) blocks as determined by the coding assignment applied to the blocks’ respective pictures. For example, blocks of I pictures may be coded non-predictively or they may be coded predictively with reference to already coded blocks of the same picture (spatial prediction or intra prediction). Pixel blocks of P pictures may be coded non-predictively, via spatial prediction or via temporal prediction with reference to one previously coded reference pictures. Blocks of B pictures may be coded non-predictively, via spatial prediction or via temporal prediction with reference to one or two previously coded reference pictures.

[71] The encoder 400, which may be for example a video coder, may perform coding operations according to a predetermined video coding technology or standard, such as ITU-T Rec. H.265. In its operation, the encoder 400 may perform various compression operations, including predictive coding operations that exploit temporal and spatial redundancies in the input video sequence. The coded video data, therefore, may conform to a syntax specified by the video coding technology or standard being used.

[72] In an embodiment, the transmitter 409 may transmit additional data with the encoded video. The source coder 403 may include such data as part of the coded video sequence.

Additional data may comprise temporal/spatial/SNR enhancement layers, other forms of redundant data such as redundant pictures and slices, Supplementary Enhancement Information (SEI) messages, Visual Usability Information (VUI) parameter set fragments, and so on. [73] Fig. 5 illustrates a simplified block-style workflow diagram 500 of exemplary view-port dependent processing an in Omnidirectional Media Application Format (OMAF) that may allow for 360-degree virtual reality (VR360) streaming described in OMAF.

[74] At acquisition block 501, video data A is acquired, such as data of multiple images and audio of same time instances in a case that the image data may represent scenes in VR360. At processing block 503, the images Bi of the same time instance are processed by one or more of being stitched, mapped onto a projected picture with respect to one or more virtual reality (VR) angles or other angles/viewpoint(s) and region-wise packed. Additionally, metadata may be created indicating any of such processed information and other information so as to assist in delivering and rendering processes.

[75] With respect to data D, at image encoding block 505, the projected pictures are encoded to data Ei and composed into a media file, and in viewport-independent streaming, and at video encoding block 504, the video pictures are encoded as data E_v as a single-layer bitstream, for example, and with respect to data B_athe audio data may also be encoded into data E_a at audio encoding block 502.

[76] The data E_a, E_v, and Ei, the entire coded bitstream Fi and/or F may be stored at a (content delivery network (CDN)/cloud) server, and typically may be fully transmitted, such as at delivery block 507 or otherwise, to an OMAF player 520 and may be fully decoded by a decoder such that at least an area of a decoded picture corresponding to a current viewport is rendered to the user at display block 516 with respect to the various metadata, file playback, and orientation/viewport metadata, such as an angle at which a user may be looking through a VR image device with respect to viewport specifications of that device, from the head/eye tracking block 508. A distinct feature of VR360 is that only a viewport may be displayed at any particular time, and such feature may be utilized to improve the performance of omnidirectional video systems, through selective delivery depending on the user’s viewport (or any other criteria, such as recommended viewport timed metadata). For example, viewport-dependent delivery may be enabled by tile-based video coding according to exemplary embodiments.

[77] As with the encoding blocks described above, the OMAF player 520 according to exemplary embodiments may similarly reverse one or more facets of such encoding with respect to the file/segment decapsulation of one or more of the data F’ and/or F’i and metadata, decode the audio data E\ at audio decoding block 510, the video data E’_v at video decoding block 513, and the image data E\ at image decoding block 514 to proceed with audio rendering of the data B’_a at audio rendering block 511 and image rendering of the data D’ at image rendering block 515 so as to output, in a VR360 format according to various metadata such as the orientation/viewport metadata, display data A’i at display block 516 and audio data A’_s at the loudspeakers/headphones block 512. The various metadata may influence ones of the data decoding and rendering processes depending on various tracks, languages, qualities, views, that may be selected by or for a user of the OMAF player 520, and it is to be understood that the order of processing described herein is presented for exemplary embodiments and may be implemented in other orders according to other exemplary embodiments.

[78] Fig. 6 illustrates a simplified block-style content flow process diagram 600 for (coded) point cloud data with view-position and angle dependent processing of point cloud data (herein “V-PCC”) with respect to capturing/generating/(de)coding/rendering/displaying 6 degree-of- freedom media. It is to be understood that the described features may be used separately or combined in any order and elements such as for encoding and decoding, among others illustrated, may be implemented by processing circuitry (e.g., one or more processors or one or more integrated circuits), and the one or more processors may execute a program that is stored in a non-transitory computer-readable medium according to exemplary embodiments.

[79] The diagram 600 illustrates exemplary embodiments for streaming of coded point cloud data according to V-PCC.

[80] At the volumetric data acquisition block 601, a real -world visual scene or a computergenerated visual scene (or combination of them) may be captured by a set of camera devices or synthesized by a computer as a volumetric data, and the volumetric data, which may have an arbitrary format, may be converted to a (quantized) point cloud data format, through image processing at the converting to point cloud block 602. For example, data from the volumetric data may be area data by area data converted into ones of points of the point cloud by pulling one or more of the values described below from the volumetric data and any associated data into a desired point cloud format according to exemplary embodiments. According to exemplary embodiments, the volumetric data may be a 3D data set of 2D images, such as slices from which a 2D projection of the 3D data set may be projected for example. According to exemplary embodiments, point cloud data formats include representations of data points in one or more various spaces and may be used to represent the volumetric data and may offer improvements with respect to sampling and data compression, such as with respect to temporal redundancies, and, for example, a point cloud data in an x, y, z, format representing, at each point of multiple points of the cloud data, color values (e.g., RGB, etc.), luminance, intensity, etc. and could be used with progressive decoding, polygon meshing, direct rendering, octree 3D representations of 2D quadtree data.

[81] At projection to images block 603, the acquired point cloud data may be projected onto 2D images and encoded as image/video pictures with video-based point cloud coding (V-PCC). The projected point cloud data may be composed of attributes, geometry, occupancy map, and other metadata used for point cloud data reconstruction such as with painter’s algorithms, ray casting algorithms, (3D) binary space partition algorithms, among others for example.

[82] At the scene generator block 609, on the other hand, a scene generator may generate some metadata to be used for rendering and displaying 6 degrees-of-freedom (DoF) media, by a director’s intention or a user’s preference for example. Such 6 DoF media may include the 360VR like 3D viewing of a scene from rotational changes on 3D axis X, Y, Z in addition to additional dimension allowing for movement front/back, up/down, and left/right with respect to a virtual experience within or at least according to point cloud coded data. The scene description metadata defines one or more scene composed of the coded point cloud data and other media data, including VR360, light field, audio, etc. and may be provided to one or more cloud servers and or file/segment encapsulation/decapsulation processing as indicated in Fig. 6 and related descriptions.

[83] After video encoding block 604 and image encoding block 605 similar to the video and image encoding described above (and as will be understood, audio encoding also may be provided as described above), file/segment encapsulation block 606 processes such that the coded point cloud data are composed into a media file for file playback or a sequence of an initialization segment and media segments for streaming according to a particular media container file format such as one or more video container formats and such as may be used with respect to DASH, among others as such descriptions represent exemplary embodiments. The file container also may include the scene description metadata, such as from the scene generator block 1109, into the file or the segments.

[84] According to exemplary embodiments, the file is encapsulated depending on the scene description metadata to include at least one view position and at least one or more angle views at that/those view position(s) each at one or more times among the 6D0F media such that such file may be transmitted on request depending on user or creator input. Further, according to exemplary embodiments, a segment of such file may include one or more portions of such file such as a portion of that 6D0F media indicating a single viewpoint and angle thereat at one or more times; however, these are merely exemplary embodiments and may be changed depending on various conditions such as network, user, creator capabilities and inputs.

[85] According to exemplary embodiments, the point cloud data is partitioned into multiple 2D/3D regions, which are independently coded such as at one or more of video encoding block 604 and image encoding block 605. Then, each independently coded partition of point cloud data may encapsulated at file/segment encapsulation block 606 as a track in a file and/or segement. According to exemplary embodiments, each point cloud track and/or a metadata track may include some useful metadata for view-position/angle dependent processing.

[86] According to exemplary embodiments, the metadata, such as included in a file and/or segment encapsulated with respect to the file/segment encapsulation block, useful for the view- position/angle dependent processing includes one or more of the following: layout information of 2D/3D partitions with indices, (dynamic) mapping information associating a 3D volume partition with one or more 2D partitions (e g. any of a tile/tile group/slice/sub-picture), 3D positions of each 3D partition on a 6DoF coordinate system, representative view position/angle lists, selected view position/angle lists corresponding to a 3D volume partition, indices of 2D/3D partitions corresponding to a selected view position/angle, quality (rank)information of each 2D/3D partition, and rendering information of each 2D/3D partition for example depending on each view position/angle. Calling on such metadata when requested, such as by a user of the V-PCC player or as directed by a content creator for the user of the V-PCC player, may allow for more efficient 1 processing with respect to specific portions of the 6D0F media desired with respect to such metadata such that the V-PCC player may deliver higher quality images of focused on portions of the 6D0F media than other portions rather than delivering unused portions of that media.

[87] From the file/ segment encapsulation block 606, the file or one or more segments of the file may be delivered using a delivery mechanism (c.g., by Dynamic Adaptive Streaming over HTTP (DASH)) directly to any of the V-PCC player 625 and a cloud server, such as at the cloud server block 607 at which the cloud server can extract one or more tracks and/or one or more specific 2D/3D partitions from a file and may merge multiple coded point cloud data into one data.

[88] According to data such as with the position/viewing angle tracking block 608, if the current viewing position and angle(s) is/are defined on a 6DoF coordinate system, at a client system, then the view-position/angle metadata may be delivered, from the file/segment encapsulation block 606 or otherwise processed from the file or segments already at the cloud server, at cloud server block 607 such that the cloud sever may extract appropriate partition(s) from the store file(s) and merge them (if necessary) depending on the metadata from the client system having the V-PCC player 625 for example, and the extracted data can be delivered to the client, as a file or segments.

[89] With respect to such data, at the file/segment decapsulation block 615, a file decapsulator processes the file or the received segments and extracts the coded bitstreams and parses the metadata, and at video decoding and image decoding blocks 610 and 611, the coded point cloud data are then decoded into decoded and reconstructed, at point cloud reconstruction block 612, to point cloud data, and the reconstructed point cloud data can be displayed at display block 614 and/or may first be composed depending on one or more various scene descriptions at scene composition block 613 with respect to scene description data according to the scene generator block 609. [90] Embodiments herein may be applied in such environments, such as 2 or more dimensional video conferencing, or hearing aids or karaoke environments or theatre environments or the like that may experience acoustic howling.

[91] For example, the ultimate goal of howling suppression is to attenuate the playback signal and send only the target signal to the loudspeaker, which, in that sense, is similar to embodiments that regard acoustic echo cancellation (AEC).

[92] Considering that deep learning is powerful at modeling complex nonlinear relationships and has been successfully introduced to suppress acoustic echo, embodiments herein employ deep learning to also serve as a powerful alternative to address AHS problems such as prior inability of deep learning in treating howling as a type of noise for speech enhancement rather even if suppressing howling in a streaming and recurrent manner.

[93] According to embodiments herein, aspects of what may be referred to as “Deep AHS” are utilized to address howling suppression. That is, AHS may be viewed herein as a supervised learning problem with the overall task to maintain only the target signal while suppressing the playback signal and background noise in a microphone recording. Considering that a playback signal and a target signal are highly correlated, embodiments herein may use a concatenation of temporal correlation (“corr ”), frequency correlation, and channel covariance (“cov .”) of input signals as feature and train an attention based recurrent neural network to estimate a complex ratio filter of the target signal.

[94] In this disclosure, embodiments consider acoustic howling suppression (AHS) as a supervised learning problem and provide a deep learning approach, called Deep AHS, to address it. Deep AHS is trained in a teacher forcing way which converts the recurrent howling suppression process into an instantaneous speech separation process to simplify the problem and accelerate the model training. Ones of the disclosed embodiments utilize trained or training of an attention based recurrent neural network to extract the target signal from the microphone recording, thus attenuating the playback signal that may lead to howling. Different training strategies are utilized for one or more embodiments and a streaming inference method implemented in a recurrent mode used to evaluate the performance of the proposed method for real-time howling suppression. Deep AHS avoids howling detection and intrinsically prohibits howling from happening, allowing for more flexibility in the design of audio systems. Experimental results show the effectiveness of the disclosed embodiments for howling suppression under different scenarios.

[95] Fig. 7 illustrates an example 700 of a single-channel acoustic amplification system 701 with a microphone and a loudspeaker coupled in the same space 702. The target speech is picked up by the microphone as s(t), which is then sent to the loudspeaker for acoustic amplification. The loudspeaker signal x(t) is played out and arrives at the microphone as a playback signal denoted as d(f) d(t) = AL(x(t)) * h(t) Eq. (1) where NL .) denotes the nonlinear distortion introduced by the loudspeaker, /z(t) represents the acoustic path from loudspeaker to microphone, and * denotes linear convolution.

[96] Fig. 7 also illustrates the signal flow 703 of an acoustic howling suppression system according to embodiments herein. For example, if without any processing, the loudspeaker signal x(t) will be a delayed and amplified version of y(t), and this playback signal d(t) will re-enter the pickup repeatedly, the corresponding microphone signal at time index t can be represented as: y(t) = s(t) -I- n(t) -I- NL[y(t — At) • G] * (t) Eq. (2) where n(t) represents the background noise, At denotes the system delay from microphone to loudspeaker, and G the gain of amplifier. The recursive relationship between y(t) and y(t - At) causes re-amplifying of playback signal and leads to a feedback loop that results in an annoying, high-pitched sound, which is known as acoustic howling. .

[97] With that being said, howling is generated in a recurrent manner rather than instantaneously. That is, howling starts as multiple playback signals and gradually forms a shrill sound after being amplified to a certain extent.

[98] As a note acoustic howling is different from acoustic echo even though inappropriately handled acoustic echo (leakage) could also result in howling. Major differences between acoustic howling and acoustic echo include that both are essentially playback signals, while howling is generated gradually, and the playback signal that leads to howling is generated from the same source as that of the target signal whereas acoustic echo is usually generated from a different source (far-end speaker), which makes the suppression of howling more challenging.

[99] Fig. 8 represents an example flowchart 800 regarding an embodiment of teacher-forced learning for howling suppression. Ideally, if the AHS method can always perfectly process microphone recording and completely attenuates the playback component in it before sending it to the loudspeaker, there will be no howling problem under any circumstances. From the speech separation point of view, it seems that AHS can be seen as a speech separation problem where the target signal s(t) is a source to be separated from the microphone signal, which is similar to the idea of how deep learning based AEC is formulated.

[100] However, to achieve howling suppression using deep learning considering the characteristics of acoustic howling, a most crucial problem is that howling is generated adaptively, and the current input depends on the previous outputs. Specifically, the existence of distortion/leakage in the current processed signal as shown in signal flow 703, will affect the playback signal received at the microphone in the next loop d(t + At). Ideally, there may be training of a deep learning model in an adaptive way by updating its parameters on a sample level. However, this requires a huge amount of computation and is hard to be realized in real applications.

[101] As such, embodiments herein employ Deep AHS to train a model for howling suppression using teacher-forced learning. Assuming that once the model is properly trained, it should attenuate the playback signal in the microphone and send only target speech to the loudspeaker. During model training, embodiments take the target speech, s(t), as the teacher signal to replace the actual output s(t) in the subsequent computation of the network, as shown in signal flow 703.

[102] By using teacher forced learning, the playback signal d(t) is then a determined signal influenced only by s(t), and the repeating summation of multiple playback signals in Eq. (2) can be simplified to a one-time playback. The corresponding microphone signal for model training can be written as: y(t) = s(t) + n(t) + NL [s(t — At) ■ 6] * h(t) Eq. (3)

[103] The microphone signal during teacher forced learning is a mixture of the target signal, background noise, and a determined one-time playback signal. And the overall problem can thus be formulated as a speech separation problem. Training Deep AHS in a teacher-forced learning way not only simplifies the overall problem but also possible to diminish the uncertainty introduced in the adaptive process of AHS and results in a robust howling suppression solution.

[104] According to exemplary embodiments, different training strategies have been explored according to embodiments herein. An example of a straightforward embodiment is to directly use the microphone signal in Eq. (3) as input at S801 and set the corresponding s(t) as the training target at S804. Such training strategy may be employed as the model trained at S806 without using a reference signal (“w/o Ref’). [105] Another embodiment involves extracting more information at S802 from input and using that additional extracted information as a reference signal during model training. Therefore, embodiments use a delayed microphone signal as additional input at S803 with the amount of delay estimated during an initial stage. Considering that the playback signal can be regarded as a delayed, scaled, nonlinear version of s(t), using a delayed microphone signal helps the model to better differentiate the target signal from playback. Such embodiment of a training strategy may be referred to as “w Ref’.

[106] In addition, there may be situations where there is always a mismatch during offline training and real-time application considering the leakage existed in s(t) . To incorporate the mismatch and better approximate the real scenarios, embodiments employ another strategy that works by fine-tuning at S805 and S807 the model using pre-processing signals, denoted as “Finetuned”. Then, the microphone signal for offline training is a modified version of Eq. (3): y'(t) = s(t) + cZ'(t) + n(t) Eq. (4) where d'(t) is the distorted playback signal generated using estimated target — Zlt). To be specific, there may be pre-processing of all the training data using a pre-trained model and then the enhanced output may be fed through the audio system to get the corresponding playback d'(t). Finally, there may be fine-tuning of the model using y'(t) ^as input. As such, the mismatch mentioned previously would be reduced slightly given that the model has seen the distortion during training.

[107] By any of the above-described embodiments, AHS may be achieved, to varying degrees, at S808 depending on one or more of those embodiments.

[108] Details of a network structure are illustrated and described with the example 1000 of Fig.

10 and the flowchart 900 of Fig. 9. The microphone signal y(t) and reference signal r(t), sampled at 16k Hz at S901, are firstly divided into 32-ms frames with 16 ms frameshift at S902. A 512- point short-time Fourier transform (STFT) is then applied at S903 to each frame, resulting in the frequency domain inputs, K(m, /) and R(m, f), with frame index m and frequency index , respectively. Then a normalized log-power spectra (LPS) may be calculated at S904 along with a correlation matrix across time frames and frequency bins of microphone (log(\ Y I²), <P^T_Y, <P^F_Y) and reference signals (log(\ R I²), <P^T_R, <P^F_R), respectively, as input features. Where <P^T_ * and are used to capture the signals' temporal and frequency dependency, which helps discriminate between howling and tonal components. Channel covariance of input signals is calculated at S905 as another input feature to account for crosscorrelation between them. A concatenation of these features is used at S906 for model training with a linear layer for feature fusion.

[109] Fig. 11 illustrates a flowchart 1100 regarding an architecture of Deep AHS for howling suppression according to embodiments of the disclosure. For example, as shown in example 1000, the network consists of three parts, where the first part 1001 employs a gated recurrent unit (GRU) layer with 257 hidden units and two ID convolution layers to estimate a complex -valued filter for playback suppression and playback estimation, respectively, at SI 101. The estimates are then applied at SI 102 on the microphone signal Y to obtain the corresponding outputs, denoted as S +~N and D

[110] The LPS of these outputs, together with the fused feature for the first part, are concatenated at SI 103 and fused to serve as the inputs for the second part 1002. Another GRU layer and two ID convolution layers are utilized to estimate two filters for estimating the playback/noise and target speech from input channels Y, S'+'N, and Rj. The covariance matrix of playback/noise $_NN and target speech $_ss are then calculated at SI 106 for the third part 1003. [111] The third part 1003 is for enhancement filter estimation, which is motivated by the idea of multi-channel signal processing. Embodiments regard the input Y and two estimates S^~N, and

D as three-channel inputs and train a self-attentive RNN to estimate the speech enhancement filters W G C^FxTx3 • These filters are then applied on the input channels to get the enhanced target speech s. Finally an inverse STFT (iSTFT) is used to get waveform s(t).

[112] The loss function for model training is defined as a combination of scale-invariance signal- to-distortion ratio (SI-SDR) in the time domain and mean absolute error (MAE) of spectrum magnitude in the frequency domain: where A is set to 10,000 to balance the value range of the two losses.

[113] Since there may always be a mismatch between the offline training and inference stage of Deep AHS. A streaming inference method, in which the output of the processor is looped back and added to the input in the following time steps, is therefore implemented to evaluate the performance of Deep AHS in a realistic and recurrent mode. Details of this streaming inference are shown in the example 1200 of Fig. 12.

[114] As such, embodiments of this disclosure provide for a deep learning approach to acoustic howling suppression. The embodiments address AHS by extracting the target signal from microphone recording using an attention based recurrent neural network with properly designed features. With the idea of teacher-forced learning, the Deep AHS model is trained offline using teacher signals and evaluated in both offline and streaming manners to show its performance for howling suppression.

[115] The technical contribution of this disclosure is fourfold. Firstly, Deep AHS formulates howling suppression, an adaptive procedure, as a supervised learning problem with the help of teacher-forced learning. It is fundamentally different from traditional AHS methods and does not require howling detection. Secondly, with such a training strategy, a streaming inference method is implemented to evaluate the performance of Deep AHS in a recurrent manner. Thirdly, Deep AHS is robust to nonlinear distortions and can achieve howling and noise suppression jointly under different scenarios, which allows for higher loop gain and brings flexibility to the design of an audio system. Lastly, multiple training strategies have been investigated for howling suppression.

[116] Embodiments of this disclosure regard acoustic howling suppression (AHS) as a supervised learning problem and employ a deep learning approach, called Deep AHS, to address it. Deep AHS is trained in a teacher forcing way which converts the recurrent howling suppression process into an instantaneous speech separation process to simplify the problem and accelerate the model training. The embodiments utilizes properly designed features and trains an attention based recurrent neural network to extract the target signal from the microphone recording, thus attenuating the playback signal that may lead to howling. Different training strategies are investigated and a streaming inference method implemented in a recurrent mode used to evaluate the performance of the proposed method for real-time howling suppression. Deep AHS avoids howling detection and intrinsically prohibits howling from happening, allowing for more flexibility in the design of audio systems. Experimental results show the effectiveness of the proposed method for howling suppression under different scenarios.

[117] The techniques described above, can be implemented as computer software using computer-readable instructions and physically stored in one or more computer-readable media or by a specifically configured one or more hardware processors. For example, FIG. 13 shows a computer system 1300 suitable for implementing certain embodiments of the disclosed subject matter. [118] The computer software can be coded using any suitable machine code or computer language, that may be subject to assembly, compilation, linking, or like mechanisms to create code comprising instructions that can be executed directly, or through interpretation, micro-code execution, and the like, by computer central processing units (CPUs), Graphics Processing Units (GPUs), and the like.

[119] The instructions can be executed on various types of computers or components thereof, including, for example, personal computers, tablet computers, servers, smartphones, gaming devices, internet of things devices, and the like.

[120] The components shown in FIG. 13 for computer system 1300 are exemplary in nature and are not intended to suggest any limitation as to the scope of use or functionality of the computer software implementing embodiments of the present disclosure. Neither should the configuration of components be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary embodiment of a computer system 1300.

[121] Computer system 1300 may include certain human interface input devices. Such a human interface input device may be responsive to input by one or more human users through, for example, tactile input (such as: keystrokes, swipes, data glove movements), audio input (such as: voice, clapping), visual input (such as: gestures), olfactory input (not depicted). The human interface devices can also be used to capture certain media not necessarily directly related to conscious input by a human, such as audio (such as: speech, music, ambient sound), images (such as: scanned images, photographic images obtain from a still image camera), video (such as two-dimensional video, three-dimensional video including stereoscopic video). [122] Input human interface devices may include one or more of (only one of each depicted): keyboard 1301, mouse 1302, trackpad 1303, touch screen 1310, joystick 1305, microphone 1306, scanner 1308, camera 1307.

[123] Computer system 1300 may also include certain human interface output devices. Such human interface output devices may be stimulating the senses of one or more human users through, for example, tactile output, sound, light, and smell/taste. Such human interface output devices may include tactile output devices (for example tactile feedback by the touch-screen 1310, or joystick 1305, but there can also be tactile feedback devices that do not serve as input devices), audio output devices (such as: speakers 1309, headphones (not depicted)), visual output devices (such as screens 1310 to include CRT screens, LCD screens, plasma screens, OLED screens, each with or without touch-screen input capability, each with or without tactile feedback capability — some of which may be capable to output two dimensional visual output or more than three dimensional output through means such as stereographic output; virtual-reality glasses (not depicted), holographic displays and smoke tanks (not depicted)), and printers (not depicted).

[124] Computer system 1300 can also include human accessible storage devices and their associated media such as optical media including CD/DVD ROM/RW 1320 with CD/DVD 1311 or the like media, thumb-drive 1322, removable hard drive or solid state drive 1323, legacy magnetic media such as tape and floppy disc (not depicted), specialized ROM/ASIC/PLD based devices such as security dongles (not depicted), and the like.

[125] Those skilled in the art should also understand that term “computer readable media” as used in connection with the presently disclosed subject matter does not encompass transmission media, carrier waves, or other transitory signals. [126] Computer system 1300 can also include interface 1399 to one or more communication networks 1398. Networks 1398 can for example be wireless, wireline, optical. Networks 1398 can further be local, wide-area, metropolitan, vehicular and industrial, real-time, delay-tolerant, and so on. Examples of networks 1398 include local area networks such as Ethernet, wireless LANs, cellular networks to include GSM, 3G, 4G, 5G, LTE and the like, TV wireline or wireless wide area digital networks to include cable TV, satellite TV, and terrestrial broadcast TV, vehicular and industrial to include CANBus, and so forth. Certain networks 1398 commonly require external network interface adapters that attached to certain general-purpose data ports or peripheral buses (1350 and 1351) (such as, for example USB ports of the computer system 1300; others are commonly integrated into the core of the computer system 1300 by attachment to a system bus as described below (for example Ethernet interface into a PC computer system or cellular network interface into a smartphone computer system). Using any of these networks 1398, computer system 1300 can communicate with other entities. Such communication can be uni-directional, receive only (for example, broadcast TV), uni-directional send-only (for example CANbusto certain CANbus devices), or bi-directional, for example to other computer systems using local or wide area digital networks. Certain protocols and protocol stacks can be used on each of those networks and network interfaces as described above.

[127] Aforementioned human interface devices, human-accessible storage devices, and network interfaces can be attached to a core 1340 of the computer system 1300.

[128] The core 1340 can include one or more Central Processing Units (CPU) 1341, Graphics Processing Units (GPU) 1342, a graphics adapter 1317, specialized programmable processing units in the form of Field Programmable Gate Areas (FPGA) 1343, hardware accelerators for certain tasks 1344, and so forth. These devices, along with Read-only memory (ROM) 1345, Random-access memory 1346, internal mass storage such as internal non-user accessible hard drives, SSDs, and the like 1347, may be connected through a system bus 1348. In some computer systems, the system bus 1348 can be accessible in the form of one or more physical plugs to enable extensions by additional CPUs, GPU, and the like. The peripheral devices can be attached either directly to the core’s system bus 1348, or through a peripheral bus 1349. Architectures for a peripheral bus include PCI, USB, and the like.

[129] CPUs 1341, GPUs 1342, FPGAs 1343, and accelerators 1344 can execute certain instructions that, in combination, can make up the aforementioned computer code. That computer code can be stored in ROM 1345 or RAM 1346. Transitional data can be also be stored in RAM 1346, whereas permanent data can be stored for example, in the internal mass storage 1347. Fast storage and retrieval to any of the memory devices can be enabled through the use of cache memory, that can be closely associated with one or more CPU 1341, GPU 1342, mass storage 1347, ROM 1345, RAM 1346, and the like.

[130] The computer readable media can have computer code thereon for performing various computer-implemented operations. The media and computer code can be those specially designed and constructed for the purposes of the present disclosure, or they can be of the kind well known and available to those having skill in the computer software arts.

[131] As an example and not by way of limitation, the computer system having architecture 1300, and specifically the core 1340 can provide functionality as a result of processor(s) (including CPUs, GPUs, FPGA, accelerators, and the like) executing software embodied in one or more tangible, computer-readable media. Such computer-readable media can be media associated with user-accessible mass storage as introduced above, as well as certain storage of the core 1340 that are of non-transitory nature, such as core-internal mass storage 1347 or ROM 1345. The software implementing various embodiments of the present disclosure can be stored in such devices and executed by core 1340. A computer-readable medium can include one or more memory devices or chips, according to particular needs. The software can cause the core 1340 and specifically the processors therein (including CPU, GPU, FPGA, and the like) to execute particular processes or particular parts of particular processes described herein, including defining data structures stored in RAM 1346 and modifying such data structures according to the processes defined by the software. In addition or as an alternative, the computer system can provide functionality as a result of logic hardwired or otherwise embodied in a circuit (for example: accelerator 1344), which can operate in place of or together with software to execute particular processes or particular parts of particular processes described herein. Reference to software can encompass logic, and vice versa, where appropriate. Reference to a computer- readable media can encompass a circuit (such as an integrated circuit (IC)) storing software for execution, a circuit embodying logic for execution, or both, where appropriate. The present disclosure encompasses any suitable combination of hardware and software.

[132] While this disclosure has described several exemplary embodiments, there are alterations, permutations, and various substitute equivalents, which fall within the scope of the disclosure. It will thus be appreciated that those skilled in the art will be able to devise numerous systems and methods which, although not explicitly shown or described herein, embody the principles of the disclosure and are thus within the spirit and scope thereof.

Claims

WHAT IS CLAIMED IS:

1. A method of acoustic howling suppression (AHS), the method performed by at least one processor and comprising: receiving an audio signal obtained from a microphone; inputting the audio signal into a neural -network based AHS model, wherein the neural- network based AHS model is trained using a training audio signal; and outputting an AHS signal from the neural-network based AHS model in which AHS is applied to the audio signal, wherein the AHS signal is a version of the audio signal in which acoustic howling noise of the audio signal is suppressed and target audio of the audio signal is sustained.

2. The method according to claim 1, wherein the neural-network based AHS model is trained with a loss function comprising a combination of a scale-invariance signal-to-distortion ratio (SI-SDR) in time domain and mean absolute error (MAE) of spectrum magnitude in frequency domain.

3. The method according to claim 1, wherein the neural -network based AHS model is trained with teacher-forced learning.

4. The method according to claim 3, wherein the neural -network based AHS model comprises a first gated recurrent unit (GRU) layer configured to apply an estimate to the audio signal.

5. The method according to claim 4, wherein the first GRU layer comprises 257 hidden units and two one-dimensional (ID) convolution layers.

6. The method according to claim 4, wherein the neural-network based AHS model further comprises a second GRU layer configured to receive an output of the first GRU and to generate a covariance matrix of the acoustic howling noise and the target audio.

7. The method according to claim 6, wherein the second GRU layer is configured to receive both the audio signal and the output from the first GRU.

8. The method according to claim 7, wherein the neural-network based AHS model further comprises an enhancement filter estimation layer comprising a self-attentive recurrent neural network (RNN) configured to provide a speech enhancement filter to an input channel of the audio signal.

9. The method according to claim 8, wherein inputs to the first GRU layer comprise the audio signal, a normalized log-power spectra (UPS) of the audio signal, a temporal correlation of the audio signal, a frequency correlation of the audio signal, and a channel covariance of the audio signal.

10. The method according to claim 9, wherein the inputs to the first GRU layer comprise a concatenation of the temporal correlation of the audio signal, the frequency correlation of the audio signal, and the channel covariance of the audio signal.

11. An apparatus for video coding, the apparatus comprising: at least one memory configured to store computer program code; at least one processor configured to access the computer program code and operate as instructed by the computer program code, the computer program code including: receiving code configured to cause the at least one processor to receive an audio signal obtained from a microphone; inputting code configured to cause the at least one processor to input the audio signal into a neural -network based AHS model, wherein the neural -network based AHS model is trained using a training audio signal; and outputting code configured to cause the at least one processor to output an AHS signal from the neural-network based AHS model in which AHS is applied to the audio signal, wherein the AHS signal is a version of the audio signal in which acoustic howling noise of the audio signal is suppressed and target audio of the audio signal is sustained.

12. The apparatus according to claim 11, wherein the neural-network based AHS model is trained with a loss function comprising a combination of a scale-invariance signal-to-distortion ratio (SI-SDR) in time domain and mean absolute error (MAE) of spectrum magnitude in frequency domain.

13. The apparatus according to claim 11, wherein the neural -network based AHS model is trained with teacher-forced learning.

14. The apparatus according to claim 13, wherein the neural-network based AHS model comprises a first gated recurrent unit (GRU) layer configured to apply an estimate to the audio signal.

15. The apparatus according to claim 14, wherein the first GRU layer comprises 257 hidden units and two one-dimensional (ID) convolution layers.

16. The apparatus according to claim 14, wherein the neural -network based AHS model further comprises a second GRU layer configured to receive an output of the first GRU and to generate a covariance matrix of the acoustic howling noise and the target audio.

17. The apparatus according to claim 16, wherein the second GRU layer is configured to receive both the audio signal and the output from the first GRU.

18. The apparatus according to claim 17, wherein the neural -network based AHS model further comprises an enhancement filter estimation layer comprising a self-attentive recurrent neural network (RNN) configured to provide a speech enhancement filter to an input channel of the audio signal.

19. The apparatus according to claim 18, wherein inputs to the first GRU layer comprise the audio signal, a normalized log-power spectra (LPS) of the audio signal, a temporal correlation of the audio signal, a frequency correlation of the audio signal, and a channel covariance of the audio signal.

20. A non-transitory computer readable medium storing a program causing a computer to: receive an audio signal obtained from a microphone; input the audio signal into a neural -network based AHS model, wherein the neural- network based AHS model is trained using a training audio signal; and output an AHS signal from the neural -network based AHS model in which AHS is applied to the audio signal, wherein the AHS signal is a version of the audio signal in which acoustic howling noise of the audio signal is suppressed and target audio of the audio signal is sustained.